In recent years, with the advance of the endoscopy technology, a variety of image diagnosis apparatuses are being developed. At present, in the image enhanced endoscopy (IEE) using magnifying endoscopic observation, optical digital methods, such as narrow band imaging (NBI) and blue laser imaging (BLI), and digital methods are being discussed, where their clinical significances as screening and qualitative diagnosis of tumors have been gradually becoming evident. While many physicians are conducting gastrointestinal endoscopic examinations, the diagnoses thereof sometimes depend on the sensibility and experience of the examiners. This raises the necessity of a computer-aided diagnosis (CAD) system that evaluates a symptom quantitatively to support the diagnosis by a physician as a “second opinion.”
In the past years, the present inventors have developed a method of analyzing spectral images (NBI images) strongly relevant to pathological tissue diagnosis from magnified colorectal endoscopic images and setting diagnosis criteria suitable for the computerized diagnosis support CAD, and developed an image recognition system (i.e., endoscopic image diagnosis support system) that can present quantitative numerals strongly relevant to the pathological tissue diagnosis. This system, using a technique called bag-of-features (BoF) or bag-of-keypoints, transforms a local feature value extracted from an image of a recognition target region (hereinafter also referred to as a scan window (SW)) in an endoscopic image to a histogram of visual words (VW), and performs image matching (i.e., feature value matching) with learning images pre-classified into pathological types (i.e., each of learning images is represented as a feature value of a histogram of visual words), thereby computing the identification probabilities of the pathological types in the identification target region.
Further, the inventors have succeeded in hardware implementation of the feature value extraction processing from an image of an identification target region and the pathologic determination (i.e., identification) processing, which are otherwise especially high in computation cost in the system, thereby permitting processing of a full HD (i.e., 1920×1080 pixels) or higher quality endoscopic image in real time with high identification precision (see Non-Patent Documents 1 and 2, for example).